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Hyperparameter Tuning with Random Search

Source: R/TunerBatchRandomSearch.R
mlr_tuners_random_search.Rd

Subclass for random search tuning.

Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

Details

The random points are sampled by paradox::generate_design_random().

Dictionary

This Tuner can be instantiated with the associated sugar function tnr():

tnr("random_search")

Parallelization

In order to support general termination criteria and parallelization, we evaluate points in a batch-fashion of size batch_size. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria. A batch contains of batch_size times resampling$iters jobs. E.g., if you set a batch size of 10 points and do a 5-fold cross validation, you can utilize up to 50 cores.

Parallelization is supported via package future (see mlr3::benchmark()'s section on parallelization for more details).

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Optimizer

This Tuner is based on bbotk::OptimizerBatchRandomSearch which can be applied on any black box optimization problem. See also the documentation of bbotk.

Parameters

batch_size

integer(1)
Maximum number of points to try in a batch.

Resources

There are several sections about hyperparameter optimization in the mlr3book.

  • An overview of all tuners can be found on our website.

  • Learn more about tuners.

The gallery features a collection of case studies and demos about optimization.

  • Use the Hyperband optimizer with different budget parameters.

Progress Bars

$optimize() supports progress bars via the package progressr combined with a Terminator. Simply wrap the function in progressr::with_progress() to enable them. We recommend to use package progress as backend; enable with progressr::handlers("progress").

See also

Package mlr3hyperband for hyperband tuning.

Other Tuner: Tuner, mlr_tuners, mlr_tuners_cmaes, mlr_tuners_design_points, mlr_tuners_gensa, mlr_tuners_grid_search, mlr_tuners_internal, mlr_tuners_irace, mlr_tuners_nloptr

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerBatchRandomSearch

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

TunerBatchRandomSearch$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

TunerBatchRandomSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Hyperparameter Optimization

# load learner and set search space
learner = lrn("classif.rpart",
  cp = to_tune(1e-04, 1e-1, logscale = TRUE)
)

# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
  tuner = tnr("random_search"),
  task = tsk("penguins"),
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10
)

# best performing hyperparameter configuration
instance$result
#>           cp learner_param_vals  x_domain classif.ce
#>        <num>             <list>    <list>      <num>
#> 1: -5.758013          <list[2]> <list[1]> 0.07826087

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp classif.ce  x_domain_cp runtime_learners           timestamp
#>         <num>      <num>        <num>            <num>              <POSc>
#>  1: -5.758013 0.07826087 0.0031573806            0.009 2024-06-30 09:41:34
#>  2: -4.141436 0.07826087 0.0159000048            0.010 2024-06-30 09:41:34
#>  3: -7.233137 0.07826087 0.0007222517            0.010 2024-06-30 09:41:34
#>  4: -4.807480 0.07826087 0.0081684173            0.011 2024-06-30 09:41:34
#>  5: -3.932615 0.07826087 0.0195923750            0.011 2024-06-30 09:41:34
#>  6: -7.006127 0.07826087 0.0009063120            0.010 2024-06-30 09:41:34
#>  7: -2.903986 0.07826087 0.0548043435            0.010 2024-06-30 09:41:34
#>  8: -7.110971 0.07826087 0.0008161022            0.010 2024-06-30 09:41:34
#>  9: -4.970553 0.07826087 0.0069393095            0.009 2024-06-30 09:41:34
#> 10: -3.614064 0.07826087 0.0269421266            0.009 2024-06-30 09:41:34
#>     batch_nr warnings errors  resample_result
#>        <int>    <int>  <int>           <list>
#>  1:        1        0      0 <ResampleResult>
#>  2:        2        0      0 <ResampleResult>
#>  3:        3        0      0 <ResampleResult>
#>  4:        4        0      0 <ResampleResult>
#>  5:        5        0      0 <ResampleResult>
#>  6:        6        0      0 <ResampleResult>
#>  7:        7        0      0 <ResampleResult>
#>  8:        8        0      0 <ResampleResult>
#>  9:        9        0      0 <ResampleResult>
#> 10:       10        0      0 <ResampleResult>

# fit final model on complete data set
learner$param_set$values = instance$result_learner_param_vals
learner$train(tsk("penguins"))